132 research outputs found
Effects of EGR rates on combustion and emission characteristics in a diesel engine with n-butanol/PODE3-4/diesel blends
An experimental investigation is conducted on the influence of EGR (Exhaust Gas Recirculation) rates (0–40%) on the combustion and emission characteristics of n-butanol/diesel/PODE3-4 blends at low-temperature combustion mode in diesel engine. The results show that at identical EGR rate, compared to D100 (diesel fuel), the peak values both of the mean cylinder pressure and the heat release rate of BD20 (20% butanol and 80% diesel in volume) are increased, ignition delay is extended, and the brake thermal efficiency is enhanced. Concerning BD20 blended with PODE3-4, the ignition delay is shortened, while both the brake thermal efficiency and the combustion efficiency increase. At the EGR rate below 30%, as the EGR rate grows, the effects on emission of soot, CO and HC are not significant, while the emission of NOx is sharply reduced; when the EGR rate is above 30%, as it grows, the emissions of soot, CO, and HC drastically rise. As EGR rate grows, the total particulate matter (PM) number concentrations of four fuels firstly decline and then rise, the total PM mass concentrations keep stable firstly and then rise drastically. As the proportion of added PODE3-4 in BD20 grows, the particle geometric mean diameters further decrease
Safe DreamerV3: Safe Reinforcement Learning with World Models
The widespread application of Reinforcement Learning (RL) in real-world
situations is yet to come to fruition, largely as a result of its failure to
satisfy the essential safety demands of such systems. Existing safe
reinforcement learning (SafeRL) methods, employing cost functions to enhance
safety, fail to achieve zero-cost in complex scenarios, including vision-only
tasks, even with comprehensive data sampling and training. To address this, we
introduce Safe DreamerV3, a novel algorithm that integrates both
Lagrangian-based and planning-based methods within a world model. Our
methodology represents a significant advancement in SafeRL as the first
algorithm to achieve nearly zero-cost in both low-dimensional and vision-only
tasks within the Safety-Gymnasium benchmark. Our project website can be found
in: https://sites.google.com/view/safedreamerv3
Biofuel trigeneration with energy storage for heating, cooling and power on farms
The drive towards net-zero carbon emissions has prompted many industries to alter the way they operate. The agriculture industry is responsible for a large proportion of the UK’s greenhouse gas emissions. Thus, the feasibility of implementing an anaerobic digestion (AD) system supplying biogas to a trigeneration system with energy storage for the provision of heating, cooling and power has been investigated in the context of a medium-scale arable farm. Two configurations – one supplied with wheat straw only, and the other supplied with wheat straw, barley straw and manure – were identified to meet the energy demands of the farm. Technical feasibility was investigated via simulations run in ECLIPSE, with the two configurations returning overall system efficiencies of 66.8% and 67.1%, respectively. Economic analyses indicated simple payback periods of 9.4 and 11 years, which fall within the expected 20-year lifetime of the project. Furthermore, the potential reduction in CO2 emissions for each scenario was determined to be 42,764 kg and 43,956 kg per year
PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior
The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information
Bi-level Actor-Critic for Multi-agent Coordination
Coordination is one of the essential problems in multi-agent systems.
Typically multi-agent reinforcement learning (MARL) methods treat agents
equally and the goal is to solve the Markov game to an arbitrary Nash
equilibrium (NE) when multiple equilibra exist, thus lacking a solution for NE
selection. In this paper, we treat agents \emph{unequally} and consider
Stackelberg equilibrium as a potentially better convergence point than Nash
equilibrium in terms of Pareto superiority, especially in cooperative
environments. Under Markov games, we formally define the bi-level reinforcement
learning problem in finding Stackelberg equilibrium. We propose a novel
bi-level actor-critic learning method that allows agents to have different
knowledge base (thus intelligent), while their actions still can be executed
simultaneously and distributedly. The convergence proof is given, while the
resulting learning algorithm is tested against the state of the arts. We found
that the proposed bi-level actor-critic algorithm successfully converged to the
Stackelberg equilibria in matrix games and find an asymmetric solution in a
highway merge environment
Dynamic Handover: Throw and Catch with Bimanual Hands
Humans throw and catch objects all the time. However, such a seemingly common
skill introduces a lot of challenges for robots to achieve: The robots need to
operate such dynamic actions at high-speed, collaborate precisely, and interact
with diverse objects. In this paper, we design a system with two multi-finger
hands attached to robot arms to solve this problem. We train our system using
Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer
to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple
novel algorithm designs including learning a trajectory prediction model for
the object. Such a model can help the robot catcher has a real-time estimation
of where the object will be heading, and then react accordingly. We conduct our
experiments with multiple objects in the real-world system, and show
significant improvements over multiple baselines. Our project page is available
at \url{https://binghao-huang.github.io/dynamic_handover/}.Comment: Accepted at CoRL 2023.
https://binghao-huang.github.io/dynamic_handover
Is Nash Equilibrium Approximator Learnable?
In this paper, we investigate the learnability of the function approximator
that approximates Nash equilibrium (NE) for games generated from a
distribution. First, we offer a generalization bound using the Probably
Approximately Correct (PAC) learning model. The bound describes the gap between
the expected loss and empirical loss of the NE approximator. Afterward, we
prove the agnostic PAC learnability of the Nash approximator. In addition to
theoretical analysis, we demonstrate an application of NE approximator in
experiments. The trained NE approximator can be used to warm-start and
accelerate classical NE solvers. Together, our results show the practicability
of approximating NE through function approximation.Comment: Accepted by AAMAS 202
Grasp Multiple Objects with One Hand
The human hand's complex kinematics allow for simultaneous grasping and
manipulation of multiple objects, essential for tasks like object transfer and
in-hand manipulation. Despite its importance, robotic multi-object grasping
remains underexplored and presents challenges in kinematics, dynamics, and
object configurations. This paper introduces MultiGrasp, a two-stage method for
multi-object grasping on a tabletop with a multi-finger dexterous hand. It
involves (i) generating pre-grasp proposals and (ii) executing the grasp and
lifting the objects. Experimental results primarily focus on dual-object
grasping and report a 44.13% success rate, showcasing adaptability to unseen
object configurations and imprecise grasps. The framework also demonstrates the
capability to grasp more than two objects, albeit at a reduced inference speed
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